Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China |
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| Institution: | 1. Research Institute for Smart Cities, School of Architecture and Urban planning, Shenzhen University, China.;2. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500AE Enschede, The Netherlands;3. Centre for Transport studies, University of Twente, PO Box 217, 7500AE Enschede, The Netherlands;1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;2. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China;3. School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA;4. Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore;5. Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning & Research Institute for Smart Cities, Shenzhen University, Shenzhen, China;6. Senseable City Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA;7. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China |
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| Abstract: | In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided. |
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